PPE-Detection: Object Detection
PPE-Detection (Personal Protective Equipment Detection) is a computer vision-based technology designed to automatically identify whether personnel are wearing essential safety gear, such as helmets, reflective vests, goggles, masks, and gloves. Using deep learning algorithms (e.g., YOLO, Faster R-CNN), this technology enables real-time detection and classification of safety equipment in high-risk environments like construction sites, factories, and healthcare facilities, significantly reducing occupational hazards. The system analyzes data from cameras or drones, integrating object detection and semantic segmentation to pinpoint non-compliant behaviors and trigger immediate alerts. Key challenges include handling occlusions in complex scenarios, multi-scale object recognition, and optimizing cross-device deployment. With advancements in edge computing and lightweight models, PPE-Detection is evolving toward cost-effective, intelligent safety management solutions, enhancing compliance and operational safety standards globally.
Source model
- Input shape: 1x3x320x192
- Number of parameters: 5.92M
- Model size: 23.64M
- Output shape: [1x21x40x24],[1x21x20x12],[1x21x10x6]
The source model can be found here
Performance Reference
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License
Source Model: BSD-3-CLAUSE
Deployable Model: APLUX-MODEL-FARM-LICENSE